Training Data: Some existing methods directly train the model on the real-world training data. Differently, some methods first utilize the synthetic dataset SynText [1]} or the large-scale real-world dataset (, MLT17 [2]}, COCO-Text [3]}, etc.) to pre-train the model, and then the model is fine-tuned on the real-world training data. Table REF  and Table REF  have shown that pre-training on external data can bring obvious performance improvements. For example, pre-training on MLT17 achieves the improvement of 4.2% in \(\textit {F-measure}\)  for PSENet-1s [4]} (Table REF ).Similarly, pre-training SynText has elevated the F-measure of about 2.9% in PAN [5]} (Table REF ). Besides, different pre-training data and epochs may also result in different detection performances obviously, but existing methods usually does not care about these inconsistencies, when comparing with other methods.
